Imagine your compliance team notices that payments are taking longer than expected to clear. They suspect fraud, and you’re the data scientist helping figure out which transactions to investigate first. Assembling that shortlist requires ingesting and cleaning raw transaction logs, training a model on past fraud, scoring new records, and scheduling the pipeline to run daily. Here’s how you can build that workflow end to end with an agent, using the Data Agent Kit inside the Antigravity IDE.
Introducing the Data Agent Kit
Data Agent Kit helps you work with Google Cloud data through a plugin for coding agents or an extension for Visual Studio Code-based IDEs. It brings two significant additions to the workflow:
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Help for your agent: MCP tools connect your agent to data services. Skills are Markdown files that give it instructions and best practices for working with your data stack
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Tools in your IDE: work with Google’s Agentic Data Cloud, from querying databases to deploying an Airflow pipeline, without leaving your editor
Loading the data
Let’s start with the raw transaction logs in Google Cloud Storage. You can ask the agent to create a notebook that loads them into BigQuery using Spark:
